{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost Parameter Tuning for Loan Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 直接调用xgboost内嵌的cv寻找最佳的参数n_estimators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先 import 必要的模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Monthly_Income</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Lead_Creation_Date</th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>Employer_Name</th>\n",
       "      <th>...</th>\n",
       "      <th>EMI_Loan_Submitted</th>\n",
       "      <th>Filled_Form</th>\n",
       "      <th>Device_Type</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Source</th>\n",
       "      <th>Var4</th>\n",
       "      <th>LoggedIn</th>\n",
       "      <th>Disbursed</th>\n",
       "      <th>Unnamed: 26</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID000002C20</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>20000</td>\n",
       "      <td>1978-05-23</td>\n",
       "      <td>44</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8691</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID000004E40</td>\n",
       "      <td>1</td>\n",
       "      <td>446</td>\n",
       "      <td>35000</td>\n",
       "      <td>1985-10-07</td>\n",
       "      <td>11</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38689</td>\n",
       "      <td>...</td>\n",
       "      <td>6762.9</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID000007H20</td>\n",
       "      <td>1</td>\n",
       "      <td>497</td>\n",
       "      <td>22500</td>\n",
       "      <td>1981-10-10</td>\n",
       "      <td>56</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1833</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID000008I30</td>\n",
       "      <td>1</td>\n",
       "      <td>568</td>\n",
       "      <td>35000</td>\n",
       "      <td>1987-11-30</td>\n",
       "      <td>26</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5698</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID000009J40</td>\n",
       "      <td>1</td>\n",
       "      <td>87</td>\n",
       "      <td>100000</td>\n",
       "      <td>1984-02-17</td>\n",
       "      <td>59</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>13513</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Gender  City  Monthly_Income         DOB  Lead_Creation_Date  \\\n",
       "0  ID000002C20       0   172           20000  1978-05-23                  44   \n",
       "1  ID000004E40       1   446           35000  1985-10-07                  11   \n",
       "2  ID000007H20       1   497           22500  1981-10-10                  56   \n",
       "3  ID000008I30       1   568           35000  1987-11-30                  26   \n",
       "4  ID000009J40       1    87          100000  1984-02-17                  59   \n",
       "\n",
       "   Loan_Amount_Applied  Loan_Tenure_Applied  Existing_EMI  Employer_Name ...   \\\n",
       "0             300000.0                  5.0           0.0           8691 ...    \n",
       "1             200000.0                  2.0           0.0          38689 ...    \n",
       "2             600000.0                  4.0           0.0           1833 ...    \n",
       "3            1000000.0                  5.0           0.0           5698 ...    \n",
       "4             500000.0                  2.0       25000.0          13513 ...    \n",
       "\n",
       "   EMI_Loan_Submitted  Filled_Form  Device_Type  Var2  Source  Var4  LoggedIn  \\\n",
       "0                 NaN            7            2     6       4     1         0   \n",
       "1              6762.9            7            2     6       4     3         0   \n",
       "2                 NaN            7            2     1      20     1         0   \n",
       "3                 NaN            7            2     1      20     3         0   \n",
       "4                 NaN            7            2     1      12     3         1   \n",
       "\n",
       "   Disbursed  Unnamed: 26  Age  \n",
       "0          0          NaN   40  \n",
       "1          0          NaN   33  \n",
       "2          0          NaN   37  \n",
       "3          0          NaN   31  \n",
       "4          0          NaN   34  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# path to where the data lies\n",
    "dpath = './'\n",
    "train = pd.read_csv(dpath +\"FE_train.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target 分布，看看各类样本分布是否均衡"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train.Disbursed);\n",
    "pyplot.xlabel('Disbursed');\n",
    "pyplot.ylabel('Number of customers');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每类样本分布不是很均匀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于电脑老旧，故取1%的数据用于完成训练测试任务，集中精力放在参数调优的学习上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "y = train['Disbursed']\n",
    "X = train.drop(['Disbursed'], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.99,random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop ids and get labels\n",
    "y_train = y_train\n",
    "X_train = X_train.drop(['ID','DOB','LoggedIn','Unnamed: 26'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare cross validation\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "默认参数，此时学习率为0.1，比较大，观察弱分类数目的大致范围（采用默认参数配置，看看模型是过拟合还是欠拟合）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#直接调用xgboost内嵌的交叉验证（cv），可对连续的n_estimators参数进行快速交叉验证\n",
    "#而GridSearchCV只能对有限个参数进行交叉验证\n",
    "def modelfit(alg, X_train, y_train, cv_folds=None, early_stopping_rounds=10):\n",
    "    xgb_param = alg.get_xgb_params()\n",
    "    #xgb_param['num_class'] = 9\n",
    "    \n",
    "    #直接调用xgboost，而非sklarn的wrapper类\n",
    "    xgtrain = xgb.DMatrix(X_train, label = y_train)\n",
    "        \n",
    "    cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], folds =cv_folds,\n",
    "             metrics='logloss', early_stopping_rounds=early_stopping_rounds)\n",
    "  \n",
    "    cvresult.to_csv('1_nestimators.csv', index_label = 'n_estimators')\n",
    "    \n",
    "    #最佳参数n_estimators\n",
    "    n_estimators = cvresult.shape[0]\n",
    "    print (\"Best n_estimators:\", n_estimators)\n",
    "    \n",
    "    # 采用交叉验证得到的最佳参数n_estimators，训练模型\n",
    "    alg.set_params(n_estimators = n_estimators)\n",
    "    alg.fit(X_train, y_train, eval_metric='logloss')\n",
    "    # make prediction\n",
    "    # plot feature importance using built-in function\n",
    "    from xgboost import plot_importance\n",
    "    plot_importance(alg)\n",
    "    pyplot.show()\n",
    "    \n",
    "    \n",
    "    #Predict training set:\n",
    "    #train_predprob = alg.predict_proba(X_train)\n",
    "    #logloss = log_loss(y_train, train_predprob)\n",
    "\n",
    "   #Print model report:\n",
    "   # print (\"logloss of train :\" )\n",
    "   # print logloss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best n_estimators: 121\n"
     ]
    },
    {
     "data": {
      "image/png": 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2UVGUZMGURPslz08kH5cmwjIa1xveD7eOPVF+iJn9batK3ND62hg2JxiHc+TRefLbcA67t6+vIHItCMIEAoHYjBw5kn79+rFx40ZatGjBI488whVXXEFhYSGSaN68OX/729/KriiwQwRHXjF0lvRj4Avc2u2Hkq6/DoyVdCfOSffGzT0DPIubY98ZSCxZm4ITn5lgZmskNcUlOykXZlYk6V5gEDDNn65PSWa3/uWsLwjCBAKBLeTn5zN79uytzo0bNy5N6UBlEZafOfKSlp/dWc77Z+KEYj4AFuKc8xbM7D3cOuy3gbdwwW7v+2sbccFrT5hZsT/3Ki7IbqYXbnmK7VCK8zzM1h9sdwFDJM3AzemXlyAIEwgEAllE6JEDZpbSoVkaUZToNT9XvM7Mzirj/nuAe5LLSKqFE235RdK9w3EpSJMpc8Ip6bkbgCaR45lsvcztj/78WIIgTCAQKAfff/89F110ER988MGWKPUpU6YwatQoGjZsCMAdd9xBz549M2xp9Sb0yNMQEYn5QNKTknavhGe0wUWKTzWz/8S8Z7S/r6JsaC5pfdKIxC4VVX8gEKi+JJTd5s+fz5w5c2jdujUAV111FYWFhRQWFgYnXgWEHnl6tkSyS5oAXEKkR+3nh2VJEqnlwcw+wi1bK889F0VseItto8Z/ZfHTnyb4LASwBQKB8pBQdhs7dizglN122SX0ATJBcOTxeAO3/Ko5SYIskn6CSz0qXG7y6wEknQzcgZuHXm5mJ0qqg8sF3g7X9oPN7HlJh+LSlu6CGyU5E/gvLoHK/r6O28zscUkFwDVmNhs4FDf83gsn7nKamX3jRVsm+PteAa62FII3pVGKrTvh4gG64j4iHkiOrk8mKLvFIyiWxSO0U3yCslvNICi7pSGh9iapNvA08A+cU/wc+ImZzZLUBJiFE3z5H/AqMAKYAbwHHGdmCyXtbU4V7Q7gIzMb79egv40ThrkTmGVmE/yw9k5AT+BkM/u1t6e+ma2MOnJJBvzczF6UdBewysxulzQZmGBmEyVdAgxN58j9x8nHuFSm4DKv/V8ptvYD9vXP2dW/6y/83Hm03qDsVk6CYlk8QjvFJyi7xSMou1XTja3V3kbiesvNgYWRMqcBj0WOL8QNv5+Kc6TJdc7GRbYn6v0SaI1bdvYhcD0lqmktcRHwf8aJxyTqKMDJvoJb5534GDsLFw0PTi42od5WD1hTyns2Bz4oh61PAZ9Ezi8EepTWlkHZLR7Zqi6VbYR2ik9QdotHtv5NEZTddpht1N58hHpUkCWd0IvYVhQmcf5MM1uQdP5jP9/9M2CKpIvMbJqkjrie+RBJr5rZrUn3FflfNrgPj4r8faa01ccGXG5mUyrwWYFAIMcIym7ZQ4ha3zHeAo6X1MDPHfcF/oVbV368F4lB0t6+/BTgcu8M8dKsSGoBfG5mI3CSsO39sP06MxsPDAU6lMOuWbh5doCzt/PdUtrqz18qaWd/vqWfTw8EAjWMhLJb+/btKSws5IYbbuC6666jXbt2tG/fnunTp3Pvvfdm2sxqT+iR7wBmtlTS73HBbwJeNrPnYcsc8TN+nfi3QHecPOp9wFzvIBfhAtXOAs6VVAR8jVN6OwK4W9JmnKrbpeUw7UpgvKTfAS/hEqCUl3S2jsYne/Hnl+GypgUCgRpGUHbLDoIjT4OlCA4zs0UkCbKY2d9xKmzJZV9h62QlmNl64Dcpyg7BpUeNMsVvyWW7prLRzJ6iJFHLV7gMaSbpbNx8d0pSvVMZtm7GRenfkK7OQKAmUVxcTKdOnWjatCmTJ09m6tSpXHvttWzevJm6desyduxYDjrooEybGajGhKH16klHoFDSXOAy4HcZticQqLYMHz58ixAKwKWXXsqECRMoLCzknHPO4fbbb8+gdYGaQE47cklrKqHOAklpw/0l1ZX0N0mfSfpQ0uuSjqygZ/f3c+OJ4+1ScTOzN8zsMDNrb2bHmdmnktpJ+lrSRq/ktkHSd3HqT7YrEAg4lixZwksvvcRFF23RaUISq1atAmDlypU0aRL+6QQqlzC0Xn5G45ZcHWxmm32gWutogYjq2+Zy1t0ft+Trv7C1ituOYmbzJD2IW4o21Nt5FjBNUjszWxbXrvISBGHiEYRO4pGpdkolinLllVdy1113sXr16i3nRo8eTc+ePcnLy6NevXrMmjWrKs0M1ECqnSOX1BB4EDjAn7rSzGZI6owL3srDqaANMLMFkvJwqmptcMIoeaXUfSBwJNAv4aTN7HPg8zSqb62AW3AKaJ/5Z66RdBNurXke8CZuLvpMoBMwQdJ6X8crlIi/9CW1gtwaUqi7xWkrc0pxP8OtYx9eDrva4NbL1wWWA/3NbGlSW0UFYbip3aY4JtVoGuU5JxUonUy1U0FBwVbHM2fOpKioiNWrV1NYWMiKFSsoKCjgpptu4rbbbqNNmzZMmjSJvn37cu2111a5veCETpLtDmxLzrdTnMXm2bqRQugEF3h2jN8/APjYSoRREiIpJwFP+/2rgTF+vz2wCS+4kqLunwPPprnWHNiMCzIDaIDLQ17HH18P3OT3947cNw441ZLEXqLHuOxlXwINcR9f04DTfRmL3H8X8IdS2msw7sMgeu5K4K9x7cLlTX8TaGglQjRjSvs9BUGYeGSrKEW2kS3tNGjQIGvatKk1a9bMGjVqZHl5edazZ09r0aLFljJffPGFtW7dOmM2ZktbZTvZ2k7EFITJ6TnyNJwE3C+pELcmu56kPYD6wJOSPgDuxemUAxwHjAcws7nA3B149hdmlhhHOwrXc53hbTkfaOavdZP0llyu8RMitqTjCKDAzJaZ2Sacjvpx/tpGYLLffxf3QVEeoqI2cexqhYtyf82/1x9wevCBQI1iyJAhLFmyhEWLFjFp0iROOOEEnn/+eVauXMknn3wCwGuvvbZVIFwgUBlUu6F1XABfF3PLp7YgaSQw3cx6+2HwgsjluILzHwKHSaplqee/k1XfXjOzvkl27Ab8BdfDXSxpMLBbGc9NpyAHO67udjgwuxx2CfjQzLqU8zmBQLWndu3ajBo1ijPPPJNatWqx1157MWbMmEybFajmVMce+avAbxMHkhIyq/Vx66vBBW8leB2XCARJbXHD6ykxs89wa7JviSieHSzptBTFZwFHSzrIl9tdUktKnONySXWBPpF7VgN7pKgrnYLcDiHpTKAHMLEcdi0AGkrq4uvYWS57WyBQY+natSuTJ7uBsd69ezNv3jzmzJlDQUEBLVqUK1NxIFBuct2R7y5pSWS7GrgC6CRprqSPcHnEwc0fD5E0A5ddLMFfgbp+zfV1uCxfpXERsB/wqR+CHkWKaG5zUeD9gYm+7lnAIWb2vb9nHvAc8E7ktrHAg5IKfRBeoq6lQEJBbg7wnnkFue3gKl//f4BzgRP8kH0su3Bt1wf4s6Q5uMQpP9lOWwKBnKS4uJjDDz+cXr16AXDssceSn59Pfn4+TZo04fTTg9hhoOoIaUwDlU6rVq1swYLkPDGBZAoKCujatWumzch6sqGd7rnnHmbPns2qVau29MQTnHnmmZx22mmcd955GbKuhGxoq1wgW9tJUqw0pjnXI5dU7HuUiW2QP18g6cvEkLc/91xCNEZScx/olq7ernJ5vKsMP9w+QdI8SR9I+rcf1i7tnlIFa2I8c4vIjKQbIuf3lHTZdtQ3WNI122tPIJBrpBKBSbB69WqmTZsWeuSBKiUXg922SS8a4XvgaODfkvYEGm/vQ+TSiu6adPpXZjZve+tMwUDgGzNr55/ZCpcgZYeQdCPwi6TTT5rZn2xrkZkbgDv8/p44Ode/7OjzA4HqTCoRmATPPvssJ554IvXq1cuAZYGaSi468tKYhEvb+W/gDOAZyl7alRIzOxJA0om4NKK1cfPLl5rZhlTiKWZmkgpwwWndcM7xQjN7I81jGgNfRJ65wD+zOTDZzNr642uAumY22Bc9V9II3Nr4C8zsbR9l/mNfZ0vc+vijgFNwQX53+boKgGtw89x5ft77Q9zc94H++DUzu1bStcAvcR80z5rZzb6OG4HzgMW47GfvltaWQdktHkHZLR5V1U6plNwmT57MvvvuS8eOHVMKiEycODFlTz0QqExy0ZEnnE+CIWb2uN+fCozykd1n45TF/ri9D/JLssYCJ5rZJ5Iew6UTvQ+438xu9eXG4ZTVXvS31jazzpJ6Ajfj1ranYgzwqqQ+3vZHzew/MUyrY2Y/kXScryORvexA3AdEG1xO9DPN7DpJzwI/wwWxAWBmgyT9NjG64T8e2kaOewAHA51xS85e8M9bi2vbw3F/P++RwpEHZbfyE5Td4lFV7ZTOUb/66qs888wzbNy4kXXr1tG9e3duvPFGVq5cyZtvvslVV12VNSphOa9YVkXkejvloiMvbWi9GNcbPwvIM7NFkSnz7aEVsNDMPvHHjwL/h3Pk3SRdB+wO7I3r1SYc+TP+Z6kCLWZWKKfV3gPn7N/xy7rWp7vHM9Hf/7qken4aAeAVMyvy0fQ7Af/w5+eVZkcaevjtfX9cF+fY98D1ztcBSHohzbs9BDwELtjt8n6pVugFohQUFPDLLAy4yTYy2U7RgKiCggKGDh26JdjtwQcf5PTTT6dHjx4ZsS0V2RrElW3kejvlXLBbDCYBI4EnKqCulF8BEfGUPn5+exRbi6ds8D/LFGgxszVm9oyZXYZTmOuJk4mN/m6ShVmSlxokjjf4OjeztVDM5rLsSIFwox35fjvIzB5O8/xAoMaT0FUPBKqa6ujI3wCG4HutO8h8oHlC1AX4FU6IpTTxlNhIOlrSXn5/F9yQ+BfAN8C+kvaRtCtu2D7KWf6eY4CVZrZye54PFEna2e8ni9FMAS5IRNFLaippX5yATm9JeXLSt6du57MDgZwmKgIDrld38sknZ9CiQE0lF4fWk+fI/2FmgxIHvhc6dDvrPlHSksjxL4ABOI322jiRlAd9sFtCPGURW4unlIcDgb/6JXO1gJdwyVxM0q24oLmFuA+KKP+T9CY+2G07nw1u6HuupPfMrJ+kGX6J3is+2K01MNNPT6wBzjWz9yQ9jhOC+QL34RQIBAKBDBEEYQKVThCEiUeuz9NVFdnQTsXFxXTq1ImmTZsyefJkjj322C3L0b799ls6d+7Mc889V0YtlU82tFUukK3tFFcQJhd75IFAIJBRhg8fTuvWrVm1ahUAb7xRMjCVUHYLBKqK6jhHXiqSfpqkDFfol2dtb31rYpT5q6Q5FfXMNM/I98vdSivTX9Iy//z5kq6KUW9XSUFLPRDwBGW3QLZR43rkZjYFF8hVlZyCSw+6PO4NknYys+JyPCMf6AS8XEa5x83st5L2ARZIesrMFpdSvitufvzNctiyFUEQJh5BECYemRSEgaDsFsg+apwjrywkdQUGA8txAi3v4rKLXQ40AaZLWm5m3bzYyi04xbTPgAFmtkbSIpzASw/gfknvAA8ADYF1wK/NbL6kX+CEZoqBlbg16LfiAgGPYWuRnJSY2QpJn+KU4BZLOhX4A7ALsAKX2jUPlz2uWFLiXeYDDwIH+KquNLMZKdojCMKUkyAIE49MCsLMnDmToqIiVq9eTWFhIStWrNiq3AMPPEDPnj2zRlwk14VOqoqcbyczC9sObMAa/7Mrzqnuj5uymAkc468tAhr4/Qa4JVx1/PH1wE2RctdF6p4KHOz3jwSm+f15QFO/v6f/2R+nNlearVvK4BxxIbCbP96LkuDHi4Bhfn8wcE2kjr9H3usA4OOy2qhly5YWKJvp06dn2oScIJPtNGjQIGvatKk1a9bMGjVqZHl5edavXz8zM1u+fLntvffetn79+ozZl0z4m4pHtrYTMNti+KHQI69Y3jazJQB+iVxznNJclKNw68Vn+GVdu+CcfoLH/f11cXm+n4yo0yWSuMwAxkp6ghIVubicJakbTrXu12b2gz+/P/C4pMbepoVp7j8JaBOxqZ6kPcxs23HGQKCaMWTIEIYMGQKUKLuNHz8egCeffJJevXqx227J+k2BQOUSHHnFsiGyn07VTbikJOkkoNb6n7WA7y2FHK2ZXSLpSJx+eqGkdJK1qUjMkXcBXpL0ipl9jVPDu8fMXohME6SiFtDFzMqSkQ0EahSTJk1i0KBBZRfuTrDiAAAgAElEQVQMBCqYGhe1niGiqmmzgKMTanFyOclbJt9gZquAhX4+HDkO8/sHmtlbZnYTbk7+R2yrzFYqZjYTGIdLpQpQH5clDeD8NLYDvAr8NnFQzo+IQKDaEJTdAtlCcORVw0PAK5Kmm9ky3Fz1RElzcY79kDT39QMulDQHl5QlsTj1bknzvArb68AcYDpuyLtQ0lkx7fozMMBLrQ7GDeO/gfs4SPAiTpK1UNKxwBVAJ0lzJX2EC4YLBHKO4uJiDj/8cHr1cgrIF154IYcddhjt27enT58+rFlT5srSQCArCEPrO4iZ1fU/C4CCyPnfRvZH4oauE8fTgCNS1NU86XghsM0nvpmdkcKU71LVmXTfWFxa1sTxf32PepS/9wdcD/xFXG5zcNndBplZdFlb3A+FQCBrSRZ1uffee7csG7v66qu5//77w1B5ICcIPfIajNd4fxYoMLMDzawNcANOsj6RCCYfl5EtEKg2pBJ1SThxM2P9+vXsYArkQKDKCI68GiJpQAr1ugdSFO2GS3f6YOKEmRXi1pV/4DOy3YqLdC+UdJak/0hq6J9TS9KnkhpUyYsFAhVEQtSlVq2t/wscMGAA++23H/Pnz+fyyy/PkHWBQPkIQ+vVEDN7BHgkRtGEcE26ejZKugmnSvdbAEmH4Obu78MtRZtjZSjWBWW3eARlt3iUt52SFdomT57MvvvuS8eOHbcRAXnkkUcoLi7m8ssv5/HHH2fAgAEVYXIgUKkERx4oL2OA53GO/ALSfDBEld0aNmzIEyfXqTIDc5U1a9YwNrRTmZS3nZKd9cSJE3n11Vd55pln2LhxI+vWraN79+7ceOONW8q0bNmShx56iB//+MfkMjmvWFZF5Ho7BUdes/kQ6FNmqQhmtljSN5JOwKnN9UtT7iFctD6tWrWybEwRmG1kayrFbGNH2yl6b0LU5cUXX+Szzz7joIMOwsyYPHkyRx99dM7/PsLfVDxyvZ2CI6/ZTAPukPRrMxsFIOkIXKR6glTr00cD44FxVr7ELoFAVmJmnH/++axatQoz47DDDuOvf/1rps0KBGIRHHkNxsxMUm/gPkmDcMvPFgFXRopNBwZ5ydlEMpYXcEPqcebhA4GspWvXrlt6YjNmbJP7JxDICYIjr+GY2X+BX6a41NZfT7U+/TBckNv8SjYvEAgEAmUQlp8FyoXvuT8N/D7TtgQC5SFZya1fv360atWKtm3bcsEFF1BUVJRhCwOB7SM48gCSeksyv7SsVMzsTjNrZmbJWd0CgawmoeSWoF+/fsyfP5958+axfv16Ro8enUHrAoHtJzjyAEBfXLrVszNtSCBQGaRScuvZsyeSkETnzp1ZsmRJBi0MBLafMEdew/F5z4/Gqby9AAyWVAu4Hzgel5e8FjDGzJ6S1BG4B6iLS67S38yWlvaMIAgTjyAIE4+y2ilZAAZKlNxWr169zbWioiLGjRvH8OHDK9TOQKCqCI48cDrwDzP7RNJ3kjoALYDmQDtgX+BjYIyknXHJX04zs2U+y9qfcMIwWxEVhGnQoCE3tdtUJS+TyzTKc04qUDpltVOysMfMmTMpKipi9erVFBYWsmLFiq3KDB06lBYtWlBcXJzToiCpyHWhk6oi19tJZpZpGwIZRNJLwH1m9pqkK3C5zXfGRaU/4ss8A/wdmA+8CXzub98JWGpmPUp7RqtWrWzBggWV9QrVhlwXpagqyttOv//97xk3bhy1a9fmhx9+YNWqVZxxxhmMHz+eW265hffff59nnnlmG9316kD4m4pHtraTpHfNrFNZ5UKPvAYjaR/gBKCtJMM5ZsNlREt5C/ChmXWpIhMDgR1myJAhDBkyBChRchs/fjyjR49mypQpTJ06tVo68UDNIfz11mz6AI/5KPTmZvYj3Jz4cuBMn92sEdDVl18ANJTUBUDSzpIOzYThgcCOcskll/DNN9/QpUsX8vPzufXWWzNtUiCwXYQeec2mL3Bn0rmngdbAEuAD4BPgLWClz4bWBxghqT7u7+c+nGZ7IJD1RJXcNm0K8QiB6kFw5DUYM+ua4twIcNHsZrbGD7+/Dczz1wuB46rSzkBgeyguLqZTp040bdqUyZMnc//993Pffffx2WefsWzZMho0aJBpEwOBCiEMrdcgJBVI+mnSuSsl/SVF8cleX/0N4DYz+zpSxwJJhX7btwpMDwTKTbIAzNFHH80///lPmjVrlkGrAoGKJzjymsVEthV9Oduf3woz62pm+WbWxszGypH4e+nnr+Wb2beVbXQgUF5SCcAcfvjhNG/ePHNGBQKVRBhar1k8BdwuaVcz2yCpOdAEKJQ0FdgLt/TsD2b2vL/+Ci4DWhfcmvNyEwRh4hEEYeKR3E7lFYAJBKobwZHXIMxshaS3gZOB53G98ceB9UBvM1slqQEwS9IL/rZWwAAzuwxAEsAjkopxgXG3WwoxgiAIU36CIEw8ktupvAIwP/zwAzNmzKB+/fpVZHHmyHWhk6oi59vJzMJWgzbgXGCi3y8EOuB64fcDc/259cB+OHW3hUn3N/U/9wBeBc4r65ktW7a0QNlMnz490ybkBGW106BBg6xp06bWrFkza9SokeXl5Vm/fv22XG/WrJktW7askq3MDsLfVDyytZ2A2Rbj//UwR17zeA440Uux5pnZe0A/oCHQ0czygW+A3Xz5tdGbzewr/3M1Tu2tc1UZHgjEYciQISxZsoRFixYxadIkTjjhBMaPH59pswKBSiM48hqGma0BCoAxlAS51Qe+NbMiSd2AlGG9kmr7oXe87nov3FrzQCDrGTFiBPvvvz9Lliyhffv2WwXCBQK5TJgjr5lMBJ6hJIJ9AvCipNm4ofX5ae7bFZjinfhOwD+BUZVsayCw3UQFYK644gquuOKKzBoUCFQC5XbkkvYCfmRmcyvBnkAVYGbP4nTTE8fLcVHpqWgbKbcW6Fi51gUCgUCgPMQaWvciIPUk7Q3MwUUt31O5pgUCgYDjhx9+oHPnzhx22GH079+fm2++GYCpU6fSoUMH8vPzOeaYY/j0008zbGkgUPXEnSOvb2argDOAR8ysI3BS5ZkVCAQCJey6665MmzaNOXPmMHr0aP7xj38wa9YsLr30UiZMmEBhYSHnnHMOt99+e6ZNDQSqnLiOvLakxsAvgcmVaE/OI6k4Il9aKGlQGeVflrRnKdevlLR73PLbYW9XSSuTbD7JXzNJ4yJla0taJmmyP+4v6f6KsiUQSIck6tatC7hkJ0VFRUhCEqtWrQJg5cqVNGnSJJNmBgIZIe4c+a3AFGCGmb0jqQXwn8ozK6dZ75dwxcLMepZR5EpgPLAuZvnt4Q0z65Xi/FpcrvI8M1sPdAe+Km/lQdktHkHZrYRUam3FxcV07NiRBQsWcMUVV3DkkUcyevRoevbsSV5eHvXq1WPWrFkZsDYQyCxya84DFYWkNWZWN+lcfVwGsZ+b2QJJE4FpZjZK0iKgE06E5Qlgf1xE+G1AI2AoLg/4cjPrFilfFyef+m/gJzgHe5qZrZd0BPAwzhH/GzjFzLYErSXZ1hW4JpUjl7QGGAG8Z2ZPSXoMl7L0WDPrJak/0MnMfpvi3qiyW8eb7gvB7WXRKA++WZ9pK7KDdk3Tq659/fXX/PnPf+aKK67gkUce4eyzz6ZNmzZMmjSJxYsXc+2111ahpdnNmjVrtoxkBNKTre3UrVu3d82sU5kF46jGAC2BqcAH/rg9To8740pl2bYBxbglXIntLH++OzATt+TrH5Hyi4AGwJnAqMj5+tHrKco3BzYB+f78E8C5fv8D4Cd+/87E7y2NvV2BlUk2H+ivrfG/66dwAjGFvvxkf70/cH9ZbRKU3eKRrepS2cb06dNt8ODBdtddd1mLFi22nP/iiy+sdevWGbQs+wh/U/HI1naigpXdRgG/B4q885/Ltlm0Ao71VpIZLN/MHgcws9dwOb0fAFIpUcwDTpL0Z0nHmtnKGM9aaC4/OMC7QHM/f76Hmb3pz/89Rj1vJNn8WeKC/103B/oCL8eoKxCocJYtW8b3338PwIYNG/jnP/9J69atWblyJZ988gkAr7322lZpSwOBmkLcOfLdzextnzAjQcjuUA58CtDWuCH0vYEl0etm9omkjkBPYIikV83s1jKq3RDZLwbyiKwPr0BewA3xdwX2qYT6A4FSWbp0Keeffz7FxcWsXr2aAQMG0KtXL0aNGsWZZ55JrVq12GuvvRgzZkymTQ0Eqpy4jny5pAMBA5DUB1haaVZVT64CPgZuAMZI6mJmRYmLkpoA35nZeD833d9fWo1LULI8zkPM7H+SVks6ysxmUTEjJ2OAlWY2z8+pBwJVSvv27Xn//fcBl+0sodbWu3dvevfunUHLAoHME3do/f+AvwGHSPoKF0l9SaVZldvkJS3lulNSS9xw+u/M7A3gdeAPSfe1A96WVAjcCCQWxD4EvCJpejlsuBB4SNJMXA+9rGH6Y5Ns7hO9aGZLzGx4OZ4fqEYsXryYbt260bp1aw499FCGD3d/CnPmzKFLly60a9eOU089dcsysEAgULWU2SP3Q8KdzOwkSXWAWuYyXwVSYGY7pbnUOlLm6sh+c787xW/J9Y0ERqYov5yt5VOHRm770MzaA/h17LNLsbcAlzQl1bVtwjh9+QK/PxYYm67uQPWgdu3aDBs2jA4dOrB69Wo6duxI9+7dueiiixg6dCjHH388Y8aM4e677+a2227LtLmBQI2jzB65mW0Gfuv311a2E/fDylWOpN5eAOWQTDw/YsdWAjCllDvc2/vTFJd/5nvWHwDHUtK7T1XPYEnX+P1bE2IwMW1t7p8RqMY0btyYDh06ALDHHnvQunVrvvrqKxYsWMBxxx0HQPfu3Xn66aczaWYgUGOJO0f+mv/P/nEi+anN7LtKsSoz9MWtuT4bGJxBO7YSgCmFhL19SerJ+0j5x6PnvMP/c1IdC3Ha+Yn7bto+k0snCMLEI1sEYVKJsWy5tmgR77//PkceeSRt27blhRde4LTTTuPJJ59k8eLFVWhlIBBIEEsQRtLCFKfNzFpUuEGpBVWa4QKuGgLLgAFm9qWkU3FzzbsAK4B+ZvaNpMHAAUAL//M+MxtRyjPr4kRXugEvmNkh/nxX4BbgGyAfl/pzHjAQFyF+upl9Vop9Y3Frrp+KvpuvdzAlw+PvAucCl5MkAJPGXgGf4damvwG0MLMfJDUH/gG8BRwOfAKcZ2brvJDM4/4dAc4xs099W60xs6FRe30E/T044ZnlQH8zW+rPj8F9aKQVmwmCMOUnWwRh0omxrF+/noEDB3Luuedy3HHH8eWXXzJy5EhWrlzJ0UcfzTPPPMPzzz9f6fZlq3hHNhLaKh7Z2k4VKghTlRvOqSSfexE43+9fADzn9/ei5GPkImCY3x8MvInLn90A5+R3LuWZ5wIP+/03gQ5WIpbyPdDY1/UVcIu/NhD3gVCafWOBPsnvRokIy/646Y2ZwDGWQgAmjb3HAFP9/t+BM/x+c9zKgqP98Ricalui3hv9/nmUiLoMjpQZC/QBdvbt0NCfPwsY4/fnAsf7/bspRWwmsQVBmHhkqyiFmdnGjRutR48eNmzYsJTXFyxYYEcccUSV2JLN7ZRthLaKR7a2ExUpCCPpvFRbnHsriC6UCJuMwzkycI5wiqR5wLXAoZF7XjKzDeZybX+LkztNR19gkt+f5I8TvGNmS81sA64X/Ko/Pw/nOEuzrzTeNhcNvhmnmNa8jPJx7V1sZjP8/vgkWyZGfqbLPw7QCjdS8JqPov8DsL+Xmt3TzP7ly41LV0Gg+mBmXHjhhbRu3Zqrr94Sp8m3334LwObNm7n99tu55JKwkCUQyARx58iPiOzvBpwIvAc8VuEWxSMxHzASuMfMXogMVydIFktJ+a6S9gFOwCUHMZzOuUm6LkU9myPHm9PVGbFvEz6g0A+H71Je+1LYuxNOzvXnkm7ELS/bR9IeSc9OtqW0/W0eg4t838rZe9W4IM5fw5gxYwbjxo2jXbt25Oe7fEB33HEH//nPf3jggQcAOOOMMxgwYEAmzQwEaiyxnIeZXR499j2zquyNvYkLQhsH9MPNzYJbNpXIxnX+dtbdB3jMzH6TOCHpX8TrVZdl3yKgI04H/TTckHVZlCUAcxIwx8y2RKtLehQ4HTdffoAXm5lJSUBcgrNw2utn4Ybz07EAaJioR9LOQEsz+9CnPD3GzP7t3zVQzTnmmGMSUzrbMHDgwCq2JhAIJBNXECaZdcDBFWlIhN0lLYlsVwNXAAMkzQV+hZufBtcDf1LSG8RUPktBX+DZpHNPA+eUo4509o0Cjpf0NnAkkYj/UihLAKYsez8Gzve27A38NVJuV0lvefuuSmeAmW3EfeD8WdIc3ND/T/zlAcADXmwmC0KzAoFAoGYTN2r9RUqGVGsBbYAnzez6SrQtUE581PpkSx1Fvggn7LO9HzzbTatWrWzBggVV/dicIyo9mk0sXryY8847j6+//ppatWpx8cUXM3DgQObMmcMll1zCmjVraN68ORMmTKBevXqVbk+2tlM2EtoqHtnaTpJiRa3H7ZEPBYb5bQhwXHDiOcnzyQIyXoDmL3ErkPQnSYszJdwTqHoSym4ff/wxs2bN4oEHHuCjjz7ioosu4s4772TevHn07t2bu+++O9OmBgI1kriOvKeZ/ctvM8xsiaRkcZGsRtI+SXriiS1rs3lJeiuFve3SlTezRal64/5ac1xwYnISlbMpiWYvzRZ5ud4Xgc6xXyKQ8wRlt0Agu4kbtd4dSO6Bn5LiXNZiZitwoi45g5kdWcFVPgXcLmlXM9vgh+KbAIWSpuLW5e8M/MHMnvfXXwGm45arnW4uoxpS/GypQdktHkHZLRAIbA+lzpFLuhS4DKeQ9lnk0h7ADDM7t3LNC1Q0kl4CHvKOehAuv/jvcTnnV0lqAMzCBTM2Az4HfpJw4JF6tlHgS7oelN3KSVB2i0e2qnBlI6Gt4pGt7VQhym645V3NcUOvzSLb3nHUZsKWfRtOxW6i3y8EOuB64ffjVNsKcdHo+/nf/cI09WyjwJduC8pu8chWdSmzoOyWq4S2ike2thMVoexmZivNzbv2NbMv/H/wBtSVdEA5PiwC2cNzwImSOgB5ZvYebj14Q6CjmeXjtOV38+XjLJkLVGPMgrJbIJDNxJVoPVXSf3DZsv6FEzp5pRLtClQSZrYGl098DCVBbvWBb82sSFI33KhLIACUKLtNmzaN/Px88vPzefnll5k4cSItW7bkkEMOoUmTJkHZLRDIEHGD3W4HjgL+aWaH+//s+5ZxTyB7mYjL5JaIYJ8AvChpNm5ofX66GyXdhROf2V3SEmC0mQ2uXHMDmSQouwUC2U3c5WdF5qK+a0mqZWbTybEI8EAJZvasmcnM5vvj5WbWxcw6mdlFZtbaT6kssqTlbGZ2nZntb2a1/M/BGXmJQKWxePFiunXrRuvWrTn00EMZPnw4AIWFhRx11FHk5+fTqVMn3n777QxbGggEIH6P/Hufs/sNYIKkb3EJQQKBQDUjIQDToUMHVq9eTceOHenevTvXXXcdN998M6eccgovv/wy1113HQUFBZk2NxCo8cTtkZ+G01e/EvgHbinaqZVlVLYg6UZJH0qa68VY0q7rljRWUp9Ktqe2pOWShlTmc2LasqekyzJtR6DiSScAI4lVq1YBsHLlSpo0aZJJMwOBgCdu9rO1kpoBB5vZo5J2x6X7rLZI6gL0AjqYE09pwNZpSHe0/tpmVt5RjR64zGS/lHSDpZu4rBr2xGkMlCnvGgRh4pEpQZi4AjD33XcfP/3pT7nmmmvYvHkzb775ZhVaGQgE0hE3B/avceIeewMHAk2BB3F5yasrjYHlZrYB3DwygKSbcKMRebj0pb9Jdqjpykgq8MdHA9Mk9celBy2SVA+3jvtgMytKY1NfYDhwKS74cKZ/3hH+fB1cnvMTcSMofwZ+ilsyOMrMRko6EaedXxt4B7jUf6gswidVkdQJGGpmXSUNBg7AiQIdANxnZiNw6VAPlFQIvGZm1ya1QVQQhpvahZmYsmiU55x5VZNueDwhAHPRRRfx3nvvMWLECC688EKOP/54pk+fzhlnnMGwYcOq1liceEcY0o9HaKt45Hw7xVlsjotk3gV4P3JuXpx7c3UD6vr3/gTX6zzen987UmYccKrfHwv0KaNMAfCXyLVHcLKn4JzesFLsyQP+C+zuy47w53fBqa8d4Y/r4Zz0pbj0prUTNuHWhi/GfTyA016/0u8vAhr4/U5Agd8fjPv42BVoAKzACcg0Bz6I05ZBECYe2SRKkUoApl69erZ582YzM9u8ebPtscceGbEtm9op2wltFY9sbScqQhAmwgZzOaoBNyxMSVrTaom59dYdcU5zGfC470F388lM5gEnAIemuL20Mo9H9kfj8nvjfz5Sikm9gOlmtg7noHtL2gloBSw1s3e83avMDdmfBDzo9zGz73zZhWb2ia/zUeC4sluDl8xsg7lRiW+BRjHuCeQolkYApkmTJvzrX/8CYNq0aRx88MGZMjEQCESIG7X+L0k3AHmSuuPmRl+sPLOyAzMrxvWiC7xT/g3QHjcEvdgPO+8WvUfSbrgefLoyW5TSzGyGpOaSjgd2MrMPSjGnL3C0HwIHp5HeDedYU31UKcX50jKdbKIk+HG3pGsbIvvFxP+7CeQgCQGYdu3akZ/vVpnecccdjBo1ioEDB7Jp0yZ22203HnrooQxbGggEIP5/yIOAC4GEM3sZ15ustkhqBWw2s//4U/m4QLP2wHK/HK8PLqNYlIQTLK1MlMdwAi23lWJLPeAY4Efm5+wlDcA590uBJpKOMLN3JO2Bk9J9FbhEUoGZbZK0N07opbmkg8zsU+BXOKU+cEPrHXGKfWeWYm+C1bjkOYFqRmkCMO+++24VWxMIBMqi1KH1hJ66mW02s1Fm9gsz6+P3q/XQOm6O/FFJH0maC7TBzRePwn3QPIcLFtsKM/u+rDJJTMClDy0tJ/gZwLSEE/c8D/wc18s+CxgpaQ7wGu5jYjTwJTDXnz/HzH7ADeE/6UcYNuOCFgFuAYZLegPX6y4VcwJBMyR9IOnussoHAoFAoHIoq0f+HC47FpKeNrM4PbVqgZm9C/wkxaU/+C25fP/IfroyXVPUdwzwlP8ASGfLWFwwXfTcd7hEJ+A+Fo5KcevVfoveNxU4PMUz3gBapjg/OOm4bWT/nHQ2B7KLxYsXc9555/H1119Tq1YtLr74YgYOHMjgwYMZNWoUDRu6P6U77riDnj17ZtjaQCBQHspy5NE51RaVaUhaA8rIe13Bz9oHmOoP98P1TJf5487RgL8Ket5I4BSg3P9z+iVnbwMneee8vTbcjltmd5+kP+H09KfHvPcg3EdIkOvNctKptQFcddVVXHPNNRm2MBAIbC9lOXJLs18t8cPF+QA+SG2NmQ2txOddniwMI+kB3DrzKMPNLDmivS/wb/9zux15kj03VkQ9geyjcePGNG7cGNharS0QCOQ+ZTnywyStwvXM8/w+/tjMrF6lWpcGrzI3Bje0vAwYYGZfSjoVN6S9C269cz8z+6YUUZPyPvd84P98/W8Cv8XFGSzHzTWfghNiOc3MvpU0Htdjfc7fv8bM6ko6CRdAuBy3NK1dqrrNbHMaO2rhAtK6AW9I2sXMNvoe8vPAe7gPko+B881svc9UNh63HM6Avmb2eVK9W+z1Pf6huFiBb4H+vi2PAB7GRd/PiNNuQdktHhWp7BZXrW3GjBncf//9PPbYY3Tq1Ilhw4ax1157VYgNgUCgaijVkZtZtsqw3g88Zk4u9gJgBHA6rod6lJmZpIuA64Df+XsOwTm+PYAFkv5q6RXUtkFSW6A38BMfBf4QLg3oE7h83v8ys0GS7gEuwCmflcZRQBv/AZKu7r+nufc4YL6ZfS5pBnAy8IK/1ga40MxmSXoMt8rgPn/tf2bW2bfZPbg2S/Wuu+KU4n5uTumtHy6q/mLcXP3FfuncvaW0V1B2KycVqewWV62tffv2PPzww0hizJgxnHPOOVx//fUVYkNlkfMqXFVIaKt45Ho75ep64C64SG5wyml3+f39ccItjXE924WRe17yUd8bfPa2RsCScjzzJOAIYLYkcEpri/219Wb2it9/Fzg2Rn0zzezLGHWnoi8wye9P8scJR77QzGb5/fE4Z5pw5InI+AmU/qHRGjdS8E9vz07AEq83n2dmiZ74ONzH0TaY2UPAQwCtWrWyy/udVsrjAuCc7y+7dq20+ouKiujVqxeXXHLJVkIvCVq0aEGvXr3oWok2VAQFBQVZb2O2ENoqHrneTrnqyJNJzN+PBO4xsxckdcUtF0uwo6ImAsaY2R+3OulU7qJBcNG6t4iseBW26DPXRvZT1p3SCGlnXO+9p6Sbff17SqrjiyTHMmxPnIOAuWa21QeJd+TVPlaiOpJOrW3p0qVb5s6fffZZ2rZtm66KQCCQpcSVaM023sQNPQP0ww2pgxviTkTwnF/Bz/wnLutYA3AR7ol19qWwCCeyAs75ppuqKE/dPYB3zOxHZtbczA7Aqez93F//sZ/HhpKAuARnRc6XNr/9EdBUUmdvzy6SDvUSrT/4zHDg2j6QAyTU2qZNm0Z+fj75+flbcoq3a9eO9u3bM336dO69N+1sSSAQyFJyoUe+uw/USnAPcAUwRtK1+GA3f20wTuzkK2AW8OOKMsLM5km6BTfcXAsoAi7BJTJJx9+A572s7atsPSoQp+4vUxTvCzybdO5pXBu8A3wI/FrSwzglt6iO5u6S3sYHu5XyrhvkcquP8EpxtYFhvu4BwGhJa/07BXKAdGptYc14IJD7qPoLtNUcSlvX7T+G2pYmPFNZtGrVyhYsWFDVj805dnSeLp3oyx//+Eeef/55atWqxb777svYsWNp0qRJxRlexeT6fGZVEtoqHtnaTpLeNbNOZZXL1aH1QCCQREL05eOPP2bWrFk88MADfPTRR4MHXpkAACAASURBVFx77bXMnTuXwsJCevXqxa233pppUwOBQAVSaY5c0prKqruM5/aWZJIOKaPcPpIKU2z7VJAdV0ravYwyF0iaJ2mu1yw/Len67CTbvpQ0IV19ZvZpOpU1M9sfOE7SIF/36ZLaRJ7VX1K5umlymdtKy9gWqEIaN25Mhw4dgK1FX+rVK5F7WLt2LX4lQiAQqCbkwhx5eUkEeJ3N1lHrWxFVcaskrsQt/1qX6qKk/YEbgQ5mtlIuU1rDaJnkIRW5fOhlDrOkw8xeoGSZ2unAZFxgG0B/4ANKn/PfLoIgTDzKIwhTmuALbC36AnDjjTfy2GOPUb9+faZPj6XAGwgEcoRKmyNPpZFe2Yps3hkuwK1tfsHMDvHnu+Kye32Dc97P4LKTDcSt2T7dzD4rxb6xwGQzeyr6bpElbsuBtrg15OcCl+NU0RbgdMy3WWstqYN/Vkef9zx6rQC4xsxm+0j22WbW3Dvy3sCuuEC+v5vZLZKaA//AC+IAc4BH/Dvv69vz7ciHwN9xTnyl3yYCf8RF/K/HrdNvgwssrOvfr7+ZLZXU0du9zj/vlGgilcg7RAVhOt5036jkIoEkGuXBN+vjlW3XtH7aawnRl3PPPZfjjjtuq2sTJkxg48aNDBgwIM3d2c+aNWuoW7dK0i/kPKGt4pGt7dStW7dYc+SYWaVsOJ3y5HMv4iRDwamfPef396Lko+IiYJjfH4xbarYr0ADn5Hcu5ZnnAg/7/TdxvV2ArsD3QGNf11fALf7aQNwHQmn2jQX6JL+br3clToimFjATOMZfWwQ0KMXWnYApuMj0R4BT/7+9O4+zqrjzPv75goAs86gojArKYgQVMGxRUYOAStT4RFwSRSYGW8bEbXQyxmDMo5DREBMSRTQq4r7gHkUZRUVaxBFlsUFQWw2QiGAUN2ghNMvv+aPqwu3L7e5L0933nu7f+/XqV5/t1qlTHLruOVX1q7R9xUD/uLwXsDwujwRWAXsSvoAsJlTMnQlj1nvFfMwnVLYCTkm7jpHAzZVcU/o5m8XyaxfXzySMcwdYBBwTl/8ALK7uXujWrZu56s2cOXOn0ygvL7ehQ4faH//4x6z7ly9fbj169Njp8+RTbZRTY+FllZtCLSfCQ1y19W19d3YbwLawo/cTpvCEUBFOV5gj+xeEqGIp08xsg4UxzKmIbJXJFvEsZa6ZrbIQ3e2vbBs69TahIqwqf1V508xWWIiLXpKWVpUsPIWfAJwBvA/cEN9AVOdFM/vczNYT3iyk8rjMzN6O+VgCzIg3Qvr15ao74Q3Di5JKCG9LOkraDdjdzF6Jx92/g+m6OmSVBH354IMPti5PnTqVgw6qsvuIcy5h8t1GXmsR2WIntSFAT0lGeOI1SVdkSWdL2vqWytJMy196hDYRmgB2KH9ZEw8V7ZvAm5JeJDyZj0k/H7BrJXnKXK/J9VVGwBIzG1Bho7R7lvO7ApEK+tKrVy969w7dP377299y5513UlpaSpMmTejUqRO33XZbnnPqnKtN9V2RpyKy3U/tR2Q7gzCRyk9TGyS9Qm5P1dXlbzkhQtujhFfVzXJIay1hgpbV2XbGHuJ7m9mCuKk38LeM871JuK50x0tqS2jLHkZoAqiJVP6yrZcC7SQNMLPXY1jYbma2RNLXko42s9l4ZLeC4kFfnGuc6vLVeitJK9J+fk6IyHaupEXAjwnt07AtIturVFLx5aCyiGdn70AaleXvDuCYGBXtcCrGSa/MJOA5SZV1EW4GjJf0Xnx9fWba+cYDF0j6X0IbebrZhC8aJcATZjYvt0vbzsPALyS9JekAQpv5bTEvTQlfIK6XtDCe68j4uXOBWyS9Tvgy4apRVFRE+/btt4tjPnHiRLp3706PHj244oorKvm0c85VzSO7uTrX2CO7zZo1izZt2nDOOeeweHEYdj9z5kyuu+46pk2bRosWLfj000955513CjK6VKEp1ChchcjLKjeFWk4FF9mtPgPEZAR7+UTSx2nrzatPoX5I+vcYEGZh/H1yNcePknRjVcdU8/lTY3x6JJ2WHjQnBqfZewfT+1Z8gndVGDhwIG3btq2w7dZbb2X06NG0aNECgPbt2+cja865BiDfnd12WOzUNiPLrmMtBHmpEOwl9gQvM7PxdZyvXcxsUyX73iAMe0t3BaGHfj8zWxsnJ6mVqHKVMbP0pofTCB3h3ovrRcAC4JO6zIML3n//fV599VWuuuoqdt11V8aPr9Pb0znXgOW1It+JADFfkUOAmCrO+xPgopj+/wIXE95OrAZuA04kBDw5xcw+lfQAYTKSp+LnUwFhjgNGx8/1AHplS9vMDs+Sh8OANcT2djNbS+hwhqTZ8XMl8Sl5tpl9K360k6TphCFl95vZtQqTpTxF6Bx3OGEc+YPANYQ29rMtBJcZRRhW9gRwEnBULNMphC8+j0haDxwGfJvQVt+GMOxvZPw3+A5wZ8x3VVOhbtWYIrtVF3EtZdOmTXz55ZfMmTOHuXPn8qMf/Yi77rqrjnPnnGuI8v1EfjOhp/m9koqAmwg9sWcDR5iZxcrnCuC/4mcOIkRu+xegVNKtZrYx1xNK6kmIjnakmW2SNInQU/1RQu/5V8xstKQ/EZ5Sf1dNkkcAh8QvIJWl/VCWzy0gfCFZJmkG8KSZPZvDJRxGqIzLgbmSngXKCGO/f0R4wl4AbDCzIyWdTviysbX3u5m9Kul/qPjl5P+y7ctDC2AC8AMzWy1pBPDfhEht9wDnm9lrkiqdvDojshtX98r6sqLBKS4uzrr9k08+4Ztvvtm6v1WrVnTt2pVXXglD8svLy1m5cmWln3fblJWVeTnlyMsqN0kvp3xX5AMIr3gh9MT+fVzuSHg63IfwZLss7TPTYlCXDZJSAWLS5yuvznHAd4B5cfKIlsBHcd96M3suLs8HvptDeq+bWWre8KrSriBW9McTnqCHEOb+7m1m11Zzvulm9iWApKcIw+ueBz40s3fi9neAl+LxbwNX5nAd6Q4mvGF4KV5HU2BFDBfb0sxST+L3E75UZbu+ScS50Lt3726XjDgl22GNxvLly2nduvXWDjVFRUWsXLmSQYMG8f7779OkSRP23XffguxwU2gKtWNSIfKyyk3SyynfFXmmWgsQUwURwo3+vwobpV0IT7nZ0k4PCNM045zpQ9Gypl2ZGBBmDjBH0svArcC1FEZAmEVmVuGLTKzIfZjDDho+fDjFxcWsXr2ajh07MnbsWIqKiigqKqJnz540b96ce++912clc87VSL4r8roMEFOZl4DHJU2Ir433BFpT9axfywkBWp4kvDpvuiNppz2xb6Uw+9leZpbq9Z0tIMwCtg8IMzRGWCsnBKepaVCWqgLCvAN0kHSYhclWmgMHxoAw/0wFitmJczcqU6ZMybr9gQceqLCe5Fd7zrn8qc9Y6/UdICYrM3ubMCvYS/G8L1B1/HaA2wkR1d4kVLgbsh20g2k3I8RXfy8GXTkN+M+47w/ApTEgzB4Zn5tNaHN/C5iS9kVgR00BfhWH5HUmhIedHIeTGeELxJ9i3t4iNAFACAhzewwIk5c5551zzm3jAWFcnSukgDA33HADkydPRhK9evXi7rvvZtddM1sv8iPp7XT1xcspd15WuSnUciq4gDAu/yQVS/pexrbLJP05x8+3kjQtvkVYIqm6Hv0F5eOPP+amm25i3rx5LF68mM2bN/Pwww9X/0HnnCtg+W4j32m5BIjJN0nz2L6sz071Mq9HUwh9EqanbTuLEJimStrWE2u8mc2M7eYzJJ2Y1tO/4G3atIn169fTrFkz1q1bx7777pvvLDnn3E5JfEWeHsWtUOXyaqSePA5cK6mFmW2IbeP7AiVxLPsehLb7X5vZ03H/c8BMwlDBYWY2E8DMyiUtIAwVrFK+AsJkBmfp0KEDl19+Ofvvvz8tW7Zk6NChDB06tN7z5ZxztcnbyBsZSdOASbGiHk0IC3sl0MrM1sQhZnOAA4FOwFJCgJs5GensTuhVf5yZLc1ynvSAMP2uvvGOurysrHp12K3C+tq1a7nmmmu4+uqradOmDWPGjOGYY47h+OOPr/e8ZVNWVkabNm3ynY2C5+WUOy+r3BRqOQ0ePDinNvLEP5G7HZZ6vf50/F1EGDf+W0kDCePOO7Ctt/3fslTiu8R0bspWiUNhBoR57LHH6NOnD8OGDQNg5cqVzJkzp2A6uRRqh5tC4+WUOy+r3CS9nLyzW+PzFHCspL6EKG0LCOPB2xEmcOkN/INtgWiyzb0+CfjAzGo8E1s+7L///syZM4d169ZhZsyYMYODDz4439lyzrmd4hV5I2NmZUAxYbKaVKSS3YBPzWyjpMGEV+pZSbo2Hn9ZHWe11h1++OGcccYZ9O3bl169erFlyxbOP//8fGfLOed2ir9ab5ymEKLUnRXXHwSeib3rS9g2tWkFMRrdVXH/gtiR/WYzm1znOa4lY8eOZezYsfnOhnPO1RqvyBuhOC+50tZXE3qlZ9Mz7bgV6Z8rdKWlpZx55plb15cuXcpvfvMbLrsscS8TnHOuUl6Ruware/fulJSECLabN2+mQ4cOnHrqqXnOlXPO1S5vI68nkjbHuOapn86S+ku6Ke4fKenmuDxG0uU7mH6lcc/judZnnL/5zl1RssyYMYMDDjiATp0qbf53zrlE8ify+rM+9ghPtxyYV0/n/2uW81dL0i5mtqkuMlSfHn74YYYPH57vbDjnXK3zijyP4lzrl5vZyVUccwBwC2F42Drg383sPUldCLOg7QI8X8PztyX0Xu8a0z7fzBZJGkOI+NYZWC3pBWAYYfrWnsAfgeaEGes2ACeZ2ReVnae+IrtlRnJLKS8vZ+rUqYwbN67O8+Ccc/XNK/L60zJOEQqwzMxybaydBPzMzD6QdDjwZ2AIMAG41czuk3RRDukckHb+18zsIsKUq2+Z2TBJQ4D72Bbuth9wtJmtlzSSUIH3IYwv/xD4pZn1kXQDcA5QYUx5emS3du3a8egJrXO83JqrbD7v2bNn06VLF959913efffdOs9HTZWVlfmc5Dnwcsqdl1Vukl5OXpHXn2yv1qskqQ1wJGFu9tTmFvH3UcDpcfl+4Ppqksv2av3oVBpm9rKkPSWl4ppONbP1acfONLO1wFpJXwPPxO1vA4dmniwzsls+oybddtttXHjhhQUfuSnp0aXqi5dT7ryscpP0cvKKvLA1Ab6q4gvAzgbKzzaULJVmZkS3DWnLW9LWt1DA99G6det48cUXuf322/OdFeecqxPea72AmdkaYJmkH0KYSlTSt+Pu19gW0GVEDU8xK/XZ2F6/Op6zwWjVqhWff/45u+22W/UHO+dcAnlFXvhGAOdJWggsAVKzj1wKXCRpLiFkak2MAfpLWgT8DvjJTubVOedcPSvYV6INjZltN0eemRUT4p5jZvcA98TlMWnHLANOyPLZZVSMxva7Ks69nLQIbWnbv2DbF4P07WMy1rfmLa53rmxfIfjqq68YNWoUixcvRhJ33XUXAwZUFrjOOeeSzZ/IGxFJxZK+l7HtMkl/rkFaUyUtrr3c1Z5LL72UE044gffee4+FCxf6DGfOuQbNn8gbEEm9CD3Y020ws8Pjcmou8ulp+88CfpFD2gJkZlsknQZUGkkun9asWcOsWbO45557AGjevDnNmzeqIHbOuUbGK/IGxMzeZts48GweB66V1MLMNkjqTAj8UiJpBrAH0Az4tZk9Hfc/B8wkvMYfJulz4OeEMeKP5pKvugoIky0AzNKlS2nXrh3nnnsuCxcupF+/fkyYMIHWret+HLtzzuWDzHZ2BJNLEknTgEmxoh4N7AlcCbQyszWS9gLmAAcS5iVfChxpZnPi528g9HZ/C3jWzLZre4/HbQ0Is9de7fpdfeMdtX4tvTps38evtLSUCy+8kIkTJ3LIIYcwceJEWrduTVFRUa2fv7aVlZXRps12XSlcBi+n3HlZ5aZQy2nw4MHzzax/tQeamf80oh/g34ApcbkE6Et4Cr8ZWBS3rQf2JoRoXZb22d7AM3G5M7A4l3N269bN6suqVausU6dOW9dnzZplJ510Ur2df2fMnDkz31lIBC+n3HlZ5aZQywmYZzn8jfXObo3PU8CxkvoCLc1sAWGIWzugn4XgM/8ghGKFioFhBgD9JC0HZgPdJBXXV8Zzsffee7PffvtRWloKhFnPDjnkkDznyjnn6o63kTcyZlYWK9+7CJ3fIIxD/9TMNkoaTHilnu2ztwK3QpgalfBqfVAdZ3mHTZw4kREjRlBeXk7Xrl25++67850l55yrM16RN05TgCfZFhnuQeAZSfMIr9bfy1fGakPv3r2ZN6++Zod1zrn88oq8ETKzv5AWZ93MVlMxuEy6rJ3ZrJIgM/Vl8+bN9O/fnw4dOvDss8/mKxvOOZd33kbeAEi6StISSYsklcTpThu0CRMmeKAX55zDK/LEkzQAOBnoa2aHAscBH+1kmgX9pmbFihVMmzaNUaNG5TsrzjmXdwX9B9vlZB/CrGUbYOtrciQdC4wn/BvPBS6wEARmOdDfzFZL6g+MN7NBksYQgsN0BlZL+jFhjvPvEaY2vcPMJkrqB/wJaAOsBkaa2aqqMrgzAWGyBX257LLL+P3vf8/atWtrlKZzzjUkXpEn3wvA1ZLeB14CHgHeIExkcqyZvS/pPuAC4MZq0uoHHG1m6yVdAHQB+pjZJkltJTUDJgKnmNlnks4ErgO2i7aSERCGq3ttqtHFFRcXV1h//fXX2bhxI2vXrqWkpITPP/98u2OSqqysrMFcS13ycsqdl1Vukl5OXpEnXBxO1g/4LjCYUJGPIwRyeT8edi9wEdVX5FPNbH1cPg64zcw2xfN8IaknoYPbiyH0Ok2BrE/jZjYJmATQvXt3u2TEdpOs1cj06dOZP38+I0eO5J///Cdr1qxh8uTJPPDAA7WSfj4VFxczaNCgfGej4Hk55c7LKjdJLyevyBsAM9tMmA61WNLbVD2v+Ca29Y3YNWNfevAXEV6pk7FtiZnlbU7QcePGMW7cOCD85xs/fnyDqMSdc66mvLNbwknqLunAtE2pyGydJX0rbvsx8EpcXk54hQ5wehVJvwD8LNXxTVJboBRoFzvYIamZpB61ciHOOedqxCvy5GsD3CvpHUmLgEOA0cC5wGPxCX0LcFs8fiwwQdKrwOYq0p0M/B1YJGkhcLaZlQNnANfHbSXAkXVxUbkYNGiQjyF3zjV6/mo94cxsPtkr0xlAnyzHvwp0y7J9TMb6JsJ0pT/P2F4CDKx5jp1zztUmfyJ3eVdUVET79u3p2TNvgeKccy6xvCJ3eTdy5Eief/75fGfDOecSySvyWiRpcwyRukTSQkk/l1SjMpbUX9JNtZSvv8R8fSjp67hcIilv7dvpBg4cSNu2bfOdDeecSyRvI69d6+N83khqDzxEmCL0mh1NyMzmAbUyhZeZnRrzNAi43MxOro10c5UZ2S1btDbnnHM14xV5HTGzT2N0s7kx/GkT4HfAIKAFcIuZ3S7pEeBeM/sfAEn3AM8AnxMrXUltCBHV+hPGdo81syckDSX0Qm8B/BU418zKcs2jpO8Bo8zsh3H9REJv97MJ4VfvBo6JeTnLzD6PQ91uBvYijDsflRZ4Jj3tSiO7ZYug9Mknn/DNN98kOrrSzkp6dKn64uWUOy+r3CS+nMzMf2rpByjLsu1L4F8Jldqv47YWhKftLsCphIocoDlhwpOWhAr/2bj9euDGtDT3IFSks4DWcdsvgauryd/WNON6E8LY8D3j+qPAiYQveAacGbf/JnV+YCZwQFw+CnihunLp1q2bVWfZsmXWo0ePao9ryGbOnJnvLCSCl1PuvKxyU6jlBMyzHOoefyKve6l5v4cCh0o6I67vBhwIPAfcJKkFcAIwy0Ks8/Q0jgPOSq2Y2ZeSTiaMGX8tHtsceH1HMmZmWyQ9BJwt6UFCoJjhMc+bgMfioQ8AD0naHTgCeCItf34POedcHvkf4TokqSsh6MqnhMrxEjObnuW4YsIsY2cCU7IlRfZwqS+a2fCdzOZdwBNx+REz2xyjuWWez+I5V1vsB1Bbhg8fTnFxMatXr6Zjx46MHTuW8847rzZP4ZxzDZb3Wq8jktoRoqndHF+RTAcuiDOIIambpNbx8IcJbdPfjcdlegG4OC3tPYA5wFGpMKySWknaLtBLdczsI0J7+GjCjGkpzYDT4vLZwGwz+xJYJSnVea6JpG/v6DkzTZkyhVWrVrFx40ZWrFjhlbhzzu0Ar8hrV8vU8DPClKIvEDqjQQh5+g6wQNJi4Ha2vRF5gRAt7SULYVAzXQvsIWlxDI062Mw+A0YCU2Jo1jnAQTXM90NUnC0N4Gugr6QFwNExDxBe8f8s5mMJsNM94D0gjHPO1Zy/Wq9FZta0in1bgF/Fn8x9G4E9M7YVE2Y0w0JP9Gwzmi0BPiC0W38JXCmpqZn9pZI8bE0zw9HAHWnrxxA60W2XXzNbSmgGqDUjR47k4osv5pxzzqnNZJ1zrlHwJ/KEUuht9hShc1xXM+tHeFruuIPplADdyd42n2saO/WF0APCOOdczfkTeXINAcrNLDWrGWb2N0lDJJ0H7EuYGU3AJDO7NAaEGUNoE+8JzAf6mJlJOkHSjXHfAmJbfWzHnwj0ItwvY8zsaUkjge8T5jRvHfOTlQeEcc65uuMVeXL1IFS4FZjZqTEYS3szuzYOa3tNUpd4SJ/42ZXAa4QOc/MIr9aHAB8Cj6QleRXwspkVxeFnb0p6Ke4bABxqZl9k5sMDwuy4xAelqCdeTrnzsspN0svJK/IGQtIthLbucuBvZB+zXg68aWYr4mdKgM5AGaGz2wdx+wPESpgw/v0Hki6P67sC+8flF7NV4gBmNgmYBNC9e3e7ZMQpVeZ/+fLltG7dmkGDBu3AVTcsxcXFjfr6c+XllDsvq9wkvZy8Ik+uJcDpqRUzu0jSXoSIcX8ny5j1+Gp9Q9qmzWy7BzLHjW/9GHC6mZVmpHU4IUSrc865PPLObsn1MrCrpAvStrWKv6sas57Ne0AXSQfE9fQgM9OBS2LnOiT1qZXcpxk+fDgDBgygtLSUjh07cuedd9b2KZxzrsHyJ/KEih3UhgE3SLoC+IzwhPxLQmjVzoQx64r7hlWR1j9jm/Y0SauB2YTOcAD/DdwILIppLacWxo6nmzKlxh3mnXOu0fOKPMHMbBVpMdgzZBuzXkzaOHIzuzht+XmyBJQxs/XAT7Nsv4eKkeCcc87lgb9ad8455xLMK3LnnHMuwbwid8455xLMK3LnnHMuwRRm2HSu7khaC5RWe6DbixAi11XNyyl3Xla5KdRy6mRm7ao7yHutu/pQamb9852JQidpnpdT9byccudllZukl5O/WnfOOecSzCty55xzLsG8Inf1YVK+M5AQXk658XLKnZdVbhJdTt7ZzTnnnEswfyJ3zjnnEswrcueccy7BvCJ3dUbSCZJKJX0oaXS+81NIJO0naaakdyUtkXRp3N5W0ouSPoi/98h3XguBpKaS3pL0bFzvIumNWE6PSGqe7zzmm6TdJT0u6b14Xw3w+2l7kv4z/p9bLGmKpF2Tfj95Re7qhKSmwC3AicAhwHBJh+Q3VwVlE/BfZnYwcARwUSyf0cAMMzsQmBHXHVwKvJu2fj1wQyynL4Hz8pKrwjIBeN7MDgK+TSgvv5/SSOoA/AfQ38x6Ak0JM0gm+n7yitzVlcOAD81sqZmVAw8Dp+Q5TwXDzFaZ2YK4vJbwR7cDoYzujYfdSxXzyDcWkjoC3wcmx3UBQ4DH4yGNvpwk/R9gIHAngJmVm9lX+P2UzS5AS0m7AK2AVST8fvKK3NWVDsBHaesr4jaXQVJnoA/wBvCvcZ751Hzz7fOXs4JxI3AFsCWu7wl8ZWab4rrfW9AV+Ay4OzZBTJbUGr+fKjCzj4HxwN8JFfjXwHwSfj95Re7qirJs87GOGSS1AZ4ALjOzNfnOT6GRdDLwqZnNT9+c5dDGfm/tAvQFbjWzPsA3NPLX6NnEPgKnAF2AfYHWhOa/TIm6n7wid3VlBbBf2npHYGWe8lKQJDUjVOIPmtmTcfM/JO0T9+8DfJqv/BWIo4AfSFpOaJ4ZQnhC3z2+GgW/tyD8f1thZm/E9ccJFbvfTxUdBywzs8/MbCPwJHAkCb+fvCJ3dWUucGDsDdqc0KFkap7zVDBiO++dwLtm9qe0XVOBn8TlnwBP13feComZXWlmHc2sM+EeetnMRgAzgTPiYV5OZp8AH0nqHjcdC7yD30+Z/g4cIalV/D+YKqdE308e2c3VGUknEZ6emgJ3mdl1ec5SwZB0NPAq8Dbb2n5/RWgnfxTYn/BH54dm9kVeMllgJA0CLjezkyV1JTyhtwXeAv7NzDbkM3/5Jqk3oUNgc2ApcC7hYc3vpzSSxgJnEkaOvAWMIrSJJ/Z+8orcOeecSzB/te6cc84lmFfkzjnnXIJ5Re6cc84lmFfkzjnnXIJ5Re6cc84l2C7VH+Kcc4VH0mbC8L2UYWa2PE/ZcS5vfPiZcy6RJJWZWZt6PN8uafG4nSsY/mrdOdcgSdpH0ixJJXHu6e/G7SdIWiBpoaQZcVtbSU9JWiRpjqRD4/YxkiZJegG4L86L/gdJc+OxP83jJToH+Kt151xytZRUEpeXmdmpGfvPBqab2XWSmgKtJLUD7gAGmtkySW3jsWOBt8xsmKQhwH1A77ivH3C0ma2XdD7wtZl9R1IL4DVJL5jZsrq8UOeq4hW5cy6p1ptZ7yr2zwXuipPTPGVmJTHM66xUxZsWrvRo4PS47WVJe0raLe6bambr4/JQ4FBJqbjcuwEHAl6Ru7zxitw51yCZ2SxJA4HvA/dL+gPwFdmnqKxqatRvMo67xMym12pmndsJ3kbunGuQJHUizGV+B2Gmub7A68AxkrrEY1Kv1mcBI+K2QcDqSuaHnw5cEJ/ykdRNUus6vRDnquFP5M65hmoQ8AtJG4Ey4Bwz+yy2ukuFQgAAAGlJREFUcz8pqQlhfu7jgTHA3ZIWAevYNvVnpslAZ2BBnAbzM2BYXV6Ec9Xx4WfOOedcgvmrdeeccy7BvCJ3zjnnEswrcueccy7BvCJ3zjnnEswrcueccy7BvCJ3zjnnEswrcueccy7B/j9H70ViUXXzsAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#params = {\"objective\": \"binary:logistic\", \"eval_metric\":\"logloss\", \"num_class\": 2}\n",
    "xgb1 = XGBClassifier(\n",
    "        learning_rate =0.05,\n",
    "        n_estimators=1000,  #数值大没关系，cv会自动返回合适的n_estimators\n",
    "        max_depth=4,\n",
    "        min_child_weight=0.4,\n",
    "        gamma=0,\n",
    "        subsample=0.6,\n",
    "        colsample_bytree=1.0,\n",
    "        colsample_bylevel=0.7,\n",
    "        reg_alpha=0.01, \n",
    "        reg_lambda=1.0,        \n",
    "        objective= 'binary:logistic',\n",
    "        seed=3)\n",
    "\n",
    "modelfit(xgb1, X_train, y_train, cv_folds = kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特征重要性如上图所示。其中Monthly Income,Employer Name,Processing Fee为最重要的三个特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: from_csv is deprecated. Please use read_csv(...) instead. Note that some of the default arguments are different, so please refer to the documentation for from_csv when changing your function calls\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvresult = pd.DataFrame.from_csv('1_nestimators.csv')\n",
    "        \n",
    "# plot\n",
    "test_means = cvresult['test-logloss-mean']\n",
    "test_stds = cvresult['test-logloss-std'] \n",
    "        \n",
    "train_means = cvresult['train-logloss-mean']\n",
    "train_stds = cvresult['train-logloss-std'] \n",
    "\n",
    "x_axis = range(0, cvresult.shape[0])\n",
    "        \n",
    "pyplot.errorbar(x_axis, test_means, yerr=test_stds ,label='Test')\n",
    "pyplot.errorbar(x_axis, train_means, yerr=train_stds ,label='Train')\n",
    "pyplot.title(\"XGBoost n_estimators vs Log Loss\")\n",
    "pyplot.xlabel( 'n_estimators' )\n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig( 'n_estimators4_1.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: from_csv is deprecated. Please use read_csv(...) instead. Note that some of the default arguments are different, so please refer to the documentation for from_csv when changing your function calls\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvresult = pd.DataFrame.from_csv('1_nestimators.csv')\n",
    "\n",
    "cvresult = cvresult.iloc[20:]\n",
    "# plot\n",
    "test_means = cvresult['test-logloss-mean']\n",
    "test_stds = cvresult['test-logloss-std'] \n",
    "        \n",
    "train_means = cvresult['train-logloss-mean']\n",
    "train_stds = cvresult['train-logloss-std'] \n",
    "\n",
    "x_axis = range(20,cvresult.shape[0]+20)\n",
    "        \n",
    "fig = pyplot.figure(figsize=(10, 10), dpi=100)\n",
    "pyplot.errorbar(x_axis, test_means, yerr=test_stds ,label='Test')\n",
    "pyplot.errorbar(x_axis, train_means, yerr=train_stds ,label='Train')\n",
    "pyplot.title(\"XGBoost n_estimators vs Log Loss\")\n",
    "pyplot.xlabel( 'n_estimators' )\n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig( 'n_estimators_detail.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最佳值：learning_rate =0.05,Best n_estimators: 121"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
